Recognizing targets is a challenge to existing detection systems. A function of automatic target recognition is to find candidate targets and to separate them from clutter which commonly includes target detection, classification, and identification. Another related function is to track identified targets by updating target information (e.g., target position) over time. Tracking targets using image data is accomplished by using known processes to recognize data pixels associated with received images (broken down into individual components) of the tracked target. Conventional detection and tracking systems analyze images for pixel distortion to determine a difference to separate the background or clutter from an established or new target.
For example, target tracking in fire control and missile seeker applications locate potential targets in high background clutter areas and track the potential targets. Several problems can occur in target tracking. One problem is accomplishing target acquisition and tracking in real time. Processing requirements, especially for image data, can be large so as to prevent timely updates of the target environment. Different environments, targets, and background also pose difficulties. A target tracking system should be flexible and sophisticated enough to accomplish real time image processing.
Another problem involves eliminating potential loss-of-lock, or loss of track, of identified targets that occur in high background clutter areas. The background can be cluttered with objects of various shapes and sizes. The components derived from these objects interfere with tracked target components. Further, discrimination between true and false targets is problematic in high background clutter. Thus, target tracking systems strive to reduce the effect of background clutter and to improve target identification and tracking.
Conventional target trackers use image correlation to update target position and direction. These trackers analyze image data to determine an updated position and direction of the track target. Conventional trackers, however, can suffer from input error and from problems with image processing that impede target tracking. For example, a global shift can occur in a received target image from a previous target image. An association algorithm for a conventional target tracker does not account for this global shift, and established targets are improperly tracked. Data derived from the image can be associated with the wrong target in a multi-target tracker. Global shift problems also can be common in missile and other airborne seeker applications that change directions, altitude, flight paths, speed and the like.
Received images also are filtered and processed using conventional methods to detect components that are associated with tracked targets. Problems occur, however, when peaks of energy of the component pixels are not so readily identified compared with the background or clutter. Further, the derived components may not be readily associated with an established target, or a new target. These efforts waste processing resources. Thus, conventional target tracking systems suffer from identification and tracking errors or inefficient tracking that wastes resources. Moreover, the inability to identify targets against high background clutter impede the effectiveness of detection and tracking systems.